Overview

Brought to you by YData

Dataset statistics

Number of variables49
Number of observations50000
Missing cells12596
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.4 MiB
Average record size in memory2.4 KiB

Variable types

Numeric12
Text8
Categorical19
Boolean8
DateTime2

Alerts

Amount Spent on In-App Purchases is highly overall correlated with Transaction TypeHigh correlation
App/Game Price is highly overall correlated with Free/Paid and 1 other fieldsHigh correlation
Category is highly overall correlated with Sub_CategoryHigh correlation
Country is highly overall correlated with StateHigh correlation
Free/Paid is highly overall correlated with App/Game Price and 1 other fieldsHigh correlation
Play Pass Plan is highly overall correlated with Play Pass UserHigh correlation
Play Pass User is highly overall correlated with Play Pass PlanHigh correlation
Price Paid (with Coupon) is highly overall correlated with App/Game Price and 1 other fieldsHigh correlation
State is highly overall correlated with CountryHigh correlation
Sub_Category is highly overall correlated with CategoryHigh correlation
Transaction Type is highly overall correlated with Amount Spent on In-App PurchasesHigh correlation
Play Pass Plan has 12596 (25.2%) missing values Missing
ID is uniformly distributed Uniform
ID has unique values Unique
Phone has unique values Unique
Transaction ID has unique values Unique
Review Text has unique values Unique
App/Game Price has 24856 (49.7%) zeros Zeros
Price Paid (with Coupon) has 24856 (49.7%) zeros Zeros
Amount Spent on In-App Purchases has 30011 (60.0%) zeros Zeros
Time Since Last Use (days) has 787 (1.6%) zeros Zeros
Subscription Duration has 1967 (3.9%) zeros Zeros

Reproduction

Analysis started2025-04-07 03:34:53.354492
Analysis finished2025-04-07 03:35:19.293795
Duration25.94 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25000.5
Minimum1
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:19.354608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2500.95
Q112500.75
median25000.5
Q337500.25
95-th percentile47500.05
Maximum50000
Range49999
Interquartile range (IQR)24999.5

Descriptive statistics

Standard deviation14433.901
Coefficient of variation (CV)0.5773445
Kurtosis-1.2
Mean25000.5
Median Absolute Deviation (MAD)12500
Skewness0
Sum1.250025 × 109
Variance2.083375 × 108
MonotonicityStrictly increasing
2025-04-07T09:05:19.432988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
50000 1
< 0.1%
49999 1
< 0.1%
49998 1
< 0.1%
49997 1
< 0.1%
49996 1
< 0.1%
49995 1
< 0.1%
49994 1
< 0.1%
49993 1
< 0.1%
49992 1
< 0.1%
49991 1
< 0.1%

Name
Text

Distinct40115
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2025-04-07T09:05:19.638180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length13.28136
Min length6

Characters and Unicode

Total characters664068
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34118 ?
Unique (%)68.2%

Sample

1st rowDanielle Taylor
2nd rowMichele Williams
3rd rowNicole Patterson
4th rowChristopher Ashley
5th rowAmy Jones
ValueCountFrequency (%)
michael 1178
 
1.2%
smith 1106
 
1.1%
james 881
 
0.9%
johnson 842
 
0.8%
david 796
 
0.8%
jennifer 745
 
0.7%
john 741
 
0.7%
robert 735
 
0.7%
christopher 696
 
0.7%
williams 682
 
0.7%
Other values (1588) 93815
91.8%
2025-04-07T09:05:19.882356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 61502
 
9.3%
a 60916
 
9.2%
52217
 
7.9%
n 50130
 
7.5%
r 47789
 
7.2%
i 40263
 
6.1%
o 35931
 
5.4%
l 33725
 
5.1%
s 29689
 
4.5%
t 23080
 
3.5%
Other values (44) 228826
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 664068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 61502
 
9.3%
a 60916
 
9.2%
52217
 
7.9%
n 50130
 
7.5%
r 47789
 
7.2%
i 40263
 
6.1%
o 35931
 
5.4%
l 33725
 
5.1%
s 29689
 
4.5%
t 23080
 
3.5%
Other values (44) 228826
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 664068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 61502
 
9.3%
a 60916
 
9.2%
52217
 
7.9%
n 50130
 
7.5%
r 47789
 
7.2%
i 40263
 
6.1%
o 35931
 
5.4%
l 33725
 
5.1%
s 29689
 
4.5%
t 23080
 
3.5%
Other values (44) 228826
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 664068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 61502
 
9.3%
a 60916
 
9.2%
52217
 
7.9%
n 50130
 
7.5%
r 47789
 
7.2%
i 40263
 
6.1%
o 35931
 
5.4%
l 33725
 
5.1%
s 29689
 
4.5%
t 23080
 
3.5%
Other values (44) 228826
34.5%

Email
Text

Distinct48955
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-04-07T09:05:20.005468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length42
Median length37
Mean length21.91168
Min length12

Characters and Unicode

Total characters1095584
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48057 ?
Unique (%)96.1%

Sample

1st rowrhodespatricia@garza.com
2nd rowkendragalloway@gmail.com
3rd rowjeffrey28@yahoo.com
4th rowgeorgetracy@gmail.com
5th rowheathchad@ramirez.com
ValueCountFrequency (%)
ksmith@yahoo.com 6
 
< 0.1%
wsmith@hotmail.com 5
 
< 0.1%
fjones@yahoo.com 5
 
< 0.1%
tjohnson@gmail.com 5
 
< 0.1%
jlopez@gmail.com 5
 
< 0.1%
gjones@gmail.com 4
 
< 0.1%
sjohnson@hotmail.com 4
 
< 0.1%
john96@gmail.com 4
 
< 0.1%
rjohnson@hotmail.com 4
 
< 0.1%
ajohnson@hotmail.com 4
 
< 0.1%
Other values (48945) 49954
99.9%
2025-04-07T09:05:20.213790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 109767
 
10.0%
a 94318
 
8.6%
m 81745
 
7.5%
e 79148
 
7.2%
r 61852
 
5.6%
n 61200
 
5.6%
i 60838
 
5.6%
l 60076
 
5.5%
c 59358
 
5.4%
@ 50000
 
4.6%
Other values (29) 377282
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1095584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 109767
 
10.0%
a 94318
 
8.6%
m 81745
 
7.5%
e 79148
 
7.2%
r 61852
 
5.6%
n 61200
 
5.6%
i 60838
 
5.6%
l 60076
 
5.5%
c 59358
 
5.4%
@ 50000
 
4.6%
Other values (29) 377282
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1095584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 109767
 
10.0%
a 94318
 
8.6%
m 81745
 
7.5%
e 79148
 
7.2%
r 61852
 
5.6%
n 61200
 
5.6%
i 60838
 
5.6%
l 60076
 
5.5%
c 59358
 
5.4%
@ 50000
 
4.6%
Other values (29) 377282
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1095584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 109767
 
10.0%
a 94318
 
8.6%
m 81745
 
7.5%
e 79148
 
7.2%
r 61852
 
5.6%
n 61200
 
5.6%
i 60838
 
5.6%
l 60076
 
5.5%
c 59358
 
5.4%
@ 50000
 
4.6%
Other values (29) 377282
34.4%

Phone
Text

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-04-07T09:05:20.346678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length19
Mean length16.1749
Min length10

Characters and Unicode

Total characters808745
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50000 ?
Unique (%)100.0%

Sample

1st row833-589-0838
2nd row664-375-2553
3rd row801-922-6916
4th row(443)903-9117x182
5th row+1-329-397-3763
ValueCountFrequency (%)
7478095214 1
 
< 0.1%
1-776-923-0318x93612 1
 
< 0.1%
833-589-0838 1
 
< 0.1%
664-375-2553 1
 
< 0.1%
801-922-6916 1
 
< 0.1%
443)903-9117x182 1
 
< 0.1%
1-329-397-3763 1
 
< 0.1%
267)873-6026 1
 
< 0.1%
001-485-643-5346 1
 
< 0.1%
518-624-4935 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
2025-04-07T09:05:20.528994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 78265
9.7%
0 68117
8.4%
1 68047
8.4%
8 65001
8.0%
4 64759
8.0%
3 64645
8.0%
7 64619
8.0%
5 64567
8.0%
9 64303
8.0%
2 64286
7.9%
Other values (6) 142136
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 808745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 78265
9.7%
0 68117
8.4%
1 68047
8.4%
8 65001
8.0%
4 64759
8.0%
3 64645
8.0%
7 64619
8.0%
5 64567
8.0%
9 64303
8.0%
2 64286
7.9%
Other values (6) 142136
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 808745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 78265
9.7%
0 68117
8.4%
1 68047
8.4%
8 65001
8.0%
4 64759
8.0%
3 64645
8.0%
7 64619
8.0%
5 64567
8.0%
9 64303
8.0%
2 64286
7.9%
Other values (6) 142136
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 808745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 78265
9.7%
0 68117
8.4%
1 68047
8.4%
8 65001
8.0%
4 64759
8.0%
3 64645
8.0%
7 64619
8.0%
5 64567
8.0%
9 64303
8.0%
2 64286
7.9%
Other values (6) 142136
17.6%

Age
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.0241
Minimum13
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:20.600261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q126
median39
Q352
95-th percentile63
Maximum65
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.312321
Coefficient of variation (CV)0.39238114
Kurtosis-1.2042427
Mean39.0241
Median Absolute Deviation (MAD)13
Skewness0.00092242599
Sum1951205
Variance234.46717
MonotonicityNot monotonic
2025-04-07T09:05:20.673081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 1006
 
2.0%
51 1002
 
2.0%
49 995
 
2.0%
20 984
 
2.0%
26 982
 
2.0%
63 977
 
2.0%
44 972
 
1.9%
29 971
 
1.9%
35 971
 
1.9%
27 969
 
1.9%
Other values (43) 40171
80.3%
ValueCountFrequency (%)
13 925
1.8%
14 962
1.9%
15 965
1.9%
16 947
1.9%
17 899
1.8%
18 884
1.8%
19 963
1.9%
20 984
2.0%
21 954
1.9%
22 925
1.8%
ValueCountFrequency (%)
65 940
1.9%
64 947
1.9%
63 977
2.0%
62 951
1.9%
61 958
1.9%
60 963
1.9%
59 945
1.9%
58 924
1.8%
57 908
1.8%
56 957
1.9%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Female
16761 
Male
16756 
Other
16483 

Length

Max length6
Median length5
Mean length5.0001
Min length4

Characters and Unicode

Total characters250005
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowOther
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 16761
33.5%
Male 16756
33.5%
Other 16483
33.0%

Length

2025-04-07T09:05:20.743809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:20.804949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 16761
33.5%
male 16756
33.5%
other 16483
33.0%

Most occurring characters

ValueCountFrequency (%)
e 66761
26.7%
a 33517
13.4%
l 33517
13.4%
F 16761
 
6.7%
m 16761
 
6.7%
M 16756
 
6.7%
O 16483
 
6.6%
t 16483
 
6.6%
h 16483
 
6.6%
r 16483
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 66761
26.7%
a 33517
13.4%
l 33517
13.4%
F 16761
 
6.7%
m 16761
 
6.7%
M 16756
 
6.7%
O 16483
 
6.6%
t 16483
 
6.6%
h 16483
 
6.6%
r 16483
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 66761
26.7%
a 33517
13.4%
l 33517
13.4%
F 16761
 
6.7%
m 16761
 
6.7%
M 16756
 
6.7%
O 16483
 
6.6%
t 16483
 
6.6%
h 16483
 
6.6%
r 16483
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 66761
26.7%
a 33517
13.4%
l 33517
13.4%
F 16761
 
6.7%
m 16761
 
6.7%
M 16756
 
6.7%
O 16483
 
6.6%
t 16483
 
6.6%
h 16483
 
6.6%
r 16483
 
6.6%

Income Level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
High
16771 
Medium
16641 
Low
16588 

Length

Max length6
Median length4
Mean length4.33388
Min length3

Characters and Unicode

Total characters216694
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowMedium
3rd rowLow
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
High 16771
33.5%
Medium 16641
33.3%
Low 16588
33.2%

Length

2025-04-07T09:05:20.855419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:20.905649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 16771
33.5%
medium 16641
33.3%
low 16588
33.2%

Most occurring characters

ValueCountFrequency (%)
i 33412
15.4%
H 16771
7.7%
g 16771
7.7%
h 16771
7.7%
M 16641
7.7%
e 16641
7.7%
d 16641
7.7%
u 16641
7.7%
m 16641
7.7%
L 16588
7.7%
Other values (2) 33176
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 216694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 33412
15.4%
H 16771
7.7%
g 16771
7.7%
h 16771
7.7%
M 16641
7.7%
e 16641
7.7%
d 16641
7.7%
u 16641
7.7%
m 16641
7.7%
L 16588
7.7%
Other values (2) 33176
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 216694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 33412
15.4%
H 16771
7.7%
g 16771
7.7%
h 16771
7.7%
M 16641
7.7%
e 16641
7.7%
d 16641
7.7%
u 16641
7.7%
m 16641
7.7%
L 16588
7.7%
Other values (2) 33176
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 216694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 33412
15.4%
H 16771
7.7%
g 16771
7.7%
h 16771
7.7%
M 16641
7.7%
e 16641
7.7%
d 16641
7.7%
u 16641
7.7%
m 16641
7.7%
L 16588
7.7%
Other values (2) 33176
15.3%

Device Type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Samsung Galaxy S23
10091 
OnePlus 11
10076 
Motorola Edge
10018 
Pixel 7
9979 
Xiaomi 13
9836 

Length

Max length18
Median length10
Mean length11.42018
Min length7

Characters and Unicode

Total characters571009
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMotorola Edge
2nd rowMotorola Edge
3rd rowPixel 7
4th rowXiaomi 13
5th rowSamsung Galaxy S23

Common Values

ValueCountFrequency (%)
Samsung Galaxy S23 10091
20.2%
OnePlus 11 10076
20.2%
Motorola Edge 10018
20.0%
Pixel 7 9979
20.0%
Xiaomi 13 9836
19.7%

Length

2025-04-07T09:05:20.958292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:20.996567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
samsung 10091
9.2%
galaxy 10091
9.2%
s23 10091
9.2%
oneplus 10076
9.2%
11 10076
9.2%
motorola 10018
9.1%
edge 10018
9.1%
pixel 9979
9.1%
7 9979
9.1%
xiaomi 9836
8.9%

Most occurring characters

ValueCountFrequency (%)
60091
 
10.5%
a 50127
 
8.8%
l 40164
 
7.0%
o 39890
 
7.0%
e 30073
 
5.3%
1 29988
 
5.3%
i 29651
 
5.2%
S 20182
 
3.5%
n 20167
 
3.5%
s 20167
 
3.5%
Other values (17) 230509
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 571009
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
60091
 
10.5%
a 50127
 
8.8%
l 40164
 
7.0%
o 39890
 
7.0%
e 30073
 
5.3%
1 29988
 
5.3%
i 29651
 
5.2%
S 20182
 
3.5%
n 20167
 
3.5%
s 20167
 
3.5%
Other values (17) 230509
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 571009
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
60091
 
10.5%
a 50127
 
8.8%
l 40164
 
7.0%
o 39890
 
7.0%
e 30073
 
5.3%
1 29988
 
5.3%
i 29651
 
5.2%
S 20182
 
3.5%
n 20167
 
3.5%
s 20167
 
3.5%
Other values (17) 230509
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 571009
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
60091
 
10.5%
a 50127
 
8.8%
l 40164
 
7.0%
o 39890
 
7.0%
e 30073
 
5.3%
1 29988
 
5.3%
i 29651
 
5.2%
S 20182
 
3.5%
n 20167
 
3.5%
s 20167
 
3.5%
Other values (17) 230509
40.4%

Android Version
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
14
12668 
11
12584 
12
12495 
13
12253 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters100000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row12
3rd row11
4th row13
5th row12

Common Values

ValueCountFrequency (%)
14 12668
25.3%
11 12584
25.2%
12 12495
25.0%
13 12253
24.5%

Length

2025-04-07T09:05:21.067355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:21.109782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
14 12668
25.3%
11 12584
25.2%
12 12495
25.0%
13 12253
24.5%

Most occurring characters

ValueCountFrequency (%)
1 62584
62.6%
4 12668
 
12.7%
2 12495
 
12.5%
3 12253
 
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 62584
62.6%
4 12668
 
12.7%
2 12495
 
12.5%
3 12253
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 62584
62.6%
4 12668
 
12.7%
2 12495
 
12.5%
3 12253
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 62584
62.6%
4 12668
 
12.7%
2 12495
 
12.5%
3 12253
 
12.3%
Distinct49920
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
2025-04-07T09:05:21.232644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length77
Median length68
Mean length44.97746
Min length24

Characters and Unicode

Total characters2248873
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49840 ?
Unique (%)99.7%

Sample

1st rowSharable Bifurcated Algorithm Action
2nd rowSelf-Enabling Regional Hierarchy Online Courses
3rd rowFully-Configurable Value-Added Open Architecture Kids Learning
4th rowRobust Dedicated Collaboration To-Do List
5th rowFace-To-Face 24Hour Archive Language Learning
ValueCountFrequency (%)
learning 6674
 
2.8%
live 3405
 
1.4%
streaming 3405
 
1.4%
saver 3389
 
1.4%
battery 3389
 
1.4%
messaging 3378
 
1.4%
file 3354
 
1.4%
manager 3354
 
1.4%
kids 3342
 
1.4%
cleaner 3336
 
1.4%
Other values (329) 203072
84.6%
2025-04-07T09:05:21.535576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 243724
 
10.8%
190098
 
8.5%
i 164406
 
7.3%
a 158577
 
7.1%
n 151038
 
6.7%
t 144118
 
6.4%
r 141991
 
6.3%
o 111028
 
4.9%
l 86095
 
3.8%
s 73052
 
3.2%
Other values (47) 784746
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2248873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 243724
 
10.8%
190098
 
8.5%
i 164406
 
7.3%
a 158577
 
7.1%
n 151038
 
6.7%
t 144118
 
6.4%
r 141991
 
6.3%
o 111028
 
4.9%
l 86095
 
3.8%
s 73052
 
3.2%
Other values (47) 784746
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2248873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 243724
 
10.8%
190098
 
8.5%
i 164406
 
7.3%
a 158577
 
7.1%
n 151038
 
6.7%
t 144118
 
6.4%
r 141991
 
6.3%
o 111028
 
4.9%
l 86095
 
3.8%
s 73052
 
3.2%
Other values (47) 784746
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2248873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 243724
 
10.8%
190098
 
8.5%
i 164406
 
7.3%
a 158577
 
7.1%
n 151038
 
6.7%
t 144118
 
6.4%
r 141991
 
6.3%
o 111028
 
4.9%
l 86095
 
3.8%
s 73052
 
3.2%
Other values (47) 784746
34.9%
Distinct37143
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-04-07T09:05:21.705999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length31
Mean length16.5356
Min length6

Characters and Unicode

Total characters826780
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33109 ?
Unique (%)66.2%

Sample

1st rowLawrence-Pacheco
2nd rowReid, Ferguson and Sanchez
3rd rowKing, Tran and Dunlap
4th rowMartin, Rose and Obrien
5th rowJordan, Henderson and Owens
ValueCountFrequency (%)
and 19362
 
16.3%
plc 2796
 
2.3%
group 2766
 
2.3%
inc 2757
 
2.3%
sons 2734
 
2.3%
llc 2685
 
2.3%
ltd 2629
 
2.2%
smith 1392
 
1.2%
johnson 1105
 
0.9%
williams 922
 
0.8%
Other values (16798) 79837
67.1%
2025-04-07T09:05:21.941414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 76592
 
9.3%
68985
 
8.3%
a 68895
 
8.3%
e 60181
 
7.3%
r 54085
 
6.5%
o 52865
 
6.4%
s 39119
 
4.7%
d 36265
 
4.4%
l 36248
 
4.4%
i 33353
 
4.0%
Other values (44) 300192
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 826780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 76592
 
9.3%
68985
 
8.3%
a 68895
 
8.3%
e 60181
 
7.3%
r 54085
 
6.5%
o 52865
 
6.4%
s 39119
 
4.7%
d 36265
 
4.4%
l 36248
 
4.4%
i 33353
 
4.0%
Other values (44) 300192
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 826780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 76592
 
9.3%
68985
 
8.3%
a 68895
 
8.3%
e 60181
 
7.3%
r 54085
 
6.5%
o 52865
 
6.4%
s 39119
 
4.7%
d 36265
 
4.4%
l 36248
 
4.4%
i 33353
 
4.0%
Other values (44) 300192
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 826780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 76592
 
9.3%
68985
 
8.3%
a 68895
 
8.3%
e 60181
 
7.3%
r 54085
 
6.5%
o 52865
 
6.4%
s 39119
 
4.7%
d 36265
 
4.4%
l 36248
 
4.4%
i 33353
 
4.0%
Other values (44) 300192
36.3%

Category
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Tools
10079 
Social
10006 
Games
9993 
Education
9972 
Productivity
9950 

Length

Max length12
Median length9
Mean length7.39088
Min length5

Characters and Unicode

Total characters369544
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGames
2nd rowEducation
3rd rowEducation
4th rowProductivity
5th rowEducation

Common Values

ValueCountFrequency (%)
Tools 10079
20.2%
Social 10006
20.0%
Games 9993
20.0%
Education 9972
19.9%
Productivity 9950
19.9%

Length

2025-04-07T09:05:22.001489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:22.052080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tools 10079
20.2%
social 10006
20.0%
games 9993
20.0%
education 9972
19.9%
productivity 9950
19.9%

Most occurring characters

ValueCountFrequency (%)
o 50086
13.6%
i 39878
 
10.8%
a 29971
 
8.1%
c 29928
 
8.1%
t 29872
 
8.1%
l 20085
 
5.4%
s 20072
 
5.4%
d 19922
 
5.4%
u 19922
 
5.4%
T 10079
 
2.7%
Other values (10) 99729
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 369544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 50086
13.6%
i 39878
 
10.8%
a 29971
 
8.1%
c 29928
 
8.1%
t 29872
 
8.1%
l 20085
 
5.4%
s 20072
 
5.4%
d 19922
 
5.4%
u 19922
 
5.4%
T 10079
 
2.7%
Other values (10) 99729
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 369544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 50086
13.6%
i 39878
 
10.8%
a 29971
 
8.1%
c 29928
 
8.1%
t 29872
 
8.1%
l 20085
 
5.4%
s 20072
 
5.4%
d 19922
 
5.4%
u 19922
 
5.4%
T 10079
 
2.7%
Other values (10) 99729
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 369544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 50086
13.6%
i 39878
 
10.8%
a 29971
 
8.1%
c 29928
 
8.1%
t 29872
 
8.1%
l 20085
 
5.4%
s 20072
 
5.4%
d 19922
 
5.4%
u 19922
 
5.4%
T 10079
 
2.7%
Other values (10) 99729
27.0%

Sub_Category
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Live Streaming
3405 
Battery Saver
3389 
Messaging
3378 
File Manager
3354 
Kids Learning
3342 
Other values (12)
33132 

Length

Max length17
Median length12
Mean length10.55826
Min length3

Characters and Unicode

Total characters527913
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAction
2nd rowOnline Courses
3rd rowKids Learning
4th rowTo-Do List
5th rowLanguage Learning

Common Values

ValueCountFrequency (%)
Live Streaming 3405
 
6.8%
Battery Saver 3389
 
6.8%
Messaging 3378
 
6.8%
File Manager 3354
 
6.7%
Kids Learning 3342
 
6.7%
Cleaner 3336
 
6.7%
Language Learning 3332
 
6.7%
Note Taking 3329
 
6.7%
To-Do List 3323
 
6.6%
Online Courses 3298
 
6.6%
Other values (7) 16514
33.0%

Length

2025-04-07T09:05:22.123573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
learning 6674
 
8.3%
live 3405
 
4.3%
streaming 3405
 
4.3%
saver 3389
 
4.2%
battery 3389
 
4.2%
messaging 3378
 
4.2%
file 3354
 
4.2%
manager 3354
 
4.2%
kids 3342
 
4.2%
cleaner 3336
 
4.2%
Other values (15) 42969
53.7%

Most occurring characters

ValueCountFrequency (%)
e 57592
 
10.9%
a 55994
 
10.6%
n 48570
 
9.2%
i 38702
 
7.3%
g 35390
 
6.7%
r 35351
 
6.7%
29995
 
5.7%
t 25999
 
4.9%
s 21976
 
4.2%
o 21690
 
4.1%
Other values (26) 156654
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 527913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 57592
 
10.9%
a 55994
 
10.6%
n 48570
 
9.2%
i 38702
 
7.3%
g 35390
 
6.7%
r 35351
 
6.7%
29995
 
5.7%
t 25999
 
4.9%
s 21976
 
4.2%
o 21690
 
4.1%
Other values (26) 156654
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 527913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 57592
 
10.9%
a 55994
 
10.6%
n 48570
 
9.2%
i 38702
 
7.3%
g 35390
 
6.7%
r 35351
 
6.7%
29995
 
5.7%
t 25999
 
4.9%
s 21976
 
4.2%
o 21690
 
4.1%
Other values (26) 156654
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 527913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 57592
 
10.9%
a 55994
 
10.6%
n 48570
 
9.2%
i 38702
 
7.3%
g 35390
 
6.7%
r 35351
 
6.7%
29995
 
5.7%
t 25999
 
4.9%
s 21976
 
4.2%
o 21690
 
4.1%
Other values (26) 156654
29.7%

Free/Paid
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Paid
25144 
Free
24856 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters200000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFree
2nd rowFree
3rd rowFree
4th rowFree
5th rowFree

Common Values

ValueCountFrequency (%)
Paid 25144
50.3%
Free 24856
49.7%

Length

2025-04-07T09:05:22.186597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:22.224554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
paid 25144
50.3%
free 24856
49.7%

Most occurring characters

ValueCountFrequency (%)
e 49712
24.9%
a 25144
12.6%
P 25144
12.6%
i 25144
12.6%
d 25144
12.6%
F 24856
12.4%
r 24856
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 49712
24.9%
a 25144
12.6%
P 25144
12.6%
i 25144
12.6%
d 25144
12.6%
F 24856
12.4%
r 24856
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 49712
24.9%
a 25144
12.6%
P 25144
12.6%
i 25144
12.6%
d 25144
12.6%
F 24856
12.4%
r 24856
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 49712
24.9%
a 25144
12.6%
P 25144
12.6%
i 25144
12.6%
d 25144
12.6%
F 24856
12.4%
r 24856
12.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
False
25066 
True
24934 
ValueCountFrequency (%)
False 25066
50.1%
True 24934
49.9%
2025-04-07T09:05:22.252019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

App Size (MB)
Real number (ℝ)

Distinct31442
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.28543
Minimum5.0100002
Maximum499.98999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:22.309826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.0100002
5-th percentile29.8195
Q1127.76
median253.645
Q3376.3125
95-th percentile475.471
Maximum499.98999
Range494.97999
Interquartile range (IQR)248.5525

Descriptive statistics

Standard deviation143.00901
Coefficient of variation (CV)0.56685403
Kurtosis-1.2029449
Mean252.28543
Median Absolute Deviation (MAD)124.145
Skewness0.000900386
Sum12614271
Variance20451.577
MonotonicityNot monotonic
2025-04-07T09:05:22.386048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258.0700073 8
 
< 0.1%
292.0700073 7
 
< 0.1%
187.4700012 7
 
< 0.1%
260.6199951 6
 
< 0.1%
427.269989 6
 
< 0.1%
283.769989 6
 
< 0.1%
227.8099976 6
 
< 0.1%
91.33000183 6
 
< 0.1%
244.2799988 6
 
< 0.1%
122.9599991 6
 
< 0.1%
Other values (31432) 49936
99.9%
ValueCountFrequency (%)
5.010000229 3
< 0.1%
5.019999981 1
 
< 0.1%
5.03000021 3
< 0.1%
5.050000191 1
 
< 0.1%
5.059999943 2
< 0.1%
5.070000172 1
 
< 0.1%
5.079999924 2
< 0.1%
5.099999905 1
 
< 0.1%
5.110000134 1
 
< 0.1%
5.119999886 2
< 0.1%
ValueCountFrequency (%)
499.9899902 2
< 0.1%
499.9700012 1
< 0.1%
499.9599915 1
< 0.1%
499.9500122 1
< 0.1%
499.9400024 2
< 0.1%
499.9299927 1
< 0.1%
499.9100037 2
< 0.1%
499.8999939 2
< 0.1%
499.8900146 1
< 0.1%
499.8599854 1
< 0.1%

Total Installs
Real number (ℝ)

Distinct49886
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5002030.5
Minimum1419
Maximum9999817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:22.467453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1419
5-th percentile500625.75
Q12510841.2
median5002246.5
Q37485859
95-th percentile9506959.7
Maximum9999817
Range9998398
Interquartile range (IQR)4975017.8

Descriptive statistics

Standard deviation2883884.8
Coefficient of variation (CV)0.57654283
Kurtosis-1.1941868
Mean5002030.5
Median Absolute Deviation (MAD)2487724
Skewness-0.0014673678
Sum2.5010152 × 1011
Variance8.3167916 × 1012
MonotonicityNot monotonic
2025-04-07T09:05:22.549474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5925552 2
 
< 0.1%
622374 2
 
< 0.1%
5003952 2
 
< 0.1%
9929255 2
 
< 0.1%
7419843 2
 
< 0.1%
8943167 2
 
< 0.1%
5417227 2
 
< 0.1%
1063926 2
 
< 0.1%
618870 2
 
< 0.1%
9621757 2
 
< 0.1%
Other values (49876) 49980
> 99.9%
ValueCountFrequency (%)
1419 1
< 0.1%
1686 1
< 0.1%
1804 1
< 0.1%
1930 1
< 0.1%
1969 1
< 0.1%
1998 1
< 0.1%
2780 1
< 0.1%
2887 1
< 0.1%
2978 1
< 0.1%
3789 1
< 0.1%
ValueCountFrequency (%)
9999817 1
< 0.1%
9999752 1
< 0.1%
9999369 1
< 0.1%
9998848 1
< 0.1%
9998699 1
< 0.1%
9998500 1
< 0.1%
9998390 1
< 0.1%
9998306 1
< 0.1%
9998285 1
< 0.1%
9998263 1
< 0.1%

Transaction ID
Text

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-04-07T09:05:22.673064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters1800000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50000 ?
Unique (%)100.0%

Sample

1st row28df6ec4-ce4a-4bbd-8241-330b01a9e71f
2nd rowfd5166e6-451b-4cf3-a123-fdf77656af72
3rd rowae849217-1d53-434b-b881-39b9ae270da7
4th row8a0f4efb-edcd-465e-b638-6821f6e07cc0
5th rowa748dbcf-ac61-4e63-8dde-29a6baa4b71a
ValueCountFrequency (%)
87e5f0fe-5da8-46d2-b8b3-8a8be05fb8bc 1
 
< 0.1%
43d0a77d-5680-4454-9a12-2ecdebdb8bc7 1
 
< 0.1%
28df6ec4-ce4a-4bbd-8241-330b01a9e71f 1
 
< 0.1%
fd5166e6-451b-4cf3-a123-fdf77656af72 1
 
< 0.1%
ae849217-1d53-434b-b881-39b9ae270da7 1
 
< 0.1%
8a0f4efb-edcd-465e-b638-6821f6e07cc0 1
 
< 0.1%
a748dbcf-ac61-4e63-8dde-29a6baa4b71a 1
 
< 0.1%
f512c4c3-b253-4218-ac4a-37ea490617f2 1
 
< 0.1%
9f044aed-7552-4327-8262-7f7312922f83 1
 
< 0.1%
d450281c-6c6f-4633-a260-772317a0df49 1
 
< 0.1%
Other values (49990) 49990
> 99.9%
2025-04-07T09:05:22.848618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 200000
 
11.1%
4 143308
 
8.0%
a 106498
 
5.9%
8 106456
 
5.9%
9 106015
 
5.9%
b 105510
 
5.9%
5 94232
 
5.2%
d 94222
 
5.2%
0 94152
 
5.2%
e 94022
 
5.2%
Other values (7) 655585
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 200000
 
11.1%
4 143308
 
8.0%
a 106498
 
5.9%
8 106456
 
5.9%
9 106015
 
5.9%
b 105510
 
5.9%
5 94232
 
5.2%
d 94222
 
5.2%
0 94152
 
5.2%
e 94022
 
5.2%
Other values (7) 655585
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 200000
 
11.1%
4 143308
 
8.0%
a 106498
 
5.9%
8 106456
 
5.9%
9 106015
 
5.9%
b 105510
 
5.9%
5 94232
 
5.2%
d 94222
 
5.2%
0 94152
 
5.2%
e 94022
 
5.2%
Other values (7) 655585
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 200000
 
11.1%
4 143308
 
8.0%
a 106498
 
5.9%
8 106456
 
5.9%
9 106015
 
5.9%
b 105510
 
5.9%
5 94232
 
5.2%
d 94222
 
5.2%
0 94152
 
5.2%
e 94022
 
5.2%
Other values (7) 655585
36.4%

Transaction Type
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Subscription
10111 
Refund
10022 
Install
10003 
Ad Click
9986 
In-App Purchase
9878 

Length

Max length15
Median length8
Mean length9.59086
Min length6

Characters and Unicode

Total characters479543
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInstall
2nd rowRefund
3rd rowSubscription
4th rowAd Click
5th rowInstall

Common Values

ValueCountFrequency (%)
Subscription 10111
20.2%
Refund 10022
20.0%
Install 10003
20.0%
Ad Click 9986
20.0%
In-App Purchase 9878
19.8%

Length

2025-04-07T09:05:22.899450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:22.950583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
subscription 10111
14.5%
refund 10022
14.3%
install 10003
14.3%
ad 9986
14.3%
click 9986
14.3%
in-app 9878
14.1%
purchase 9878
14.1%

Most occurring characters

ValueCountFrequency (%)
n 40014
 
8.3%
i 30208
 
6.3%
u 30011
 
6.3%
l 29992
 
6.3%
s 29992
 
6.3%
c 29975
 
6.3%
p 29867
 
6.2%
t 20114
 
4.2%
d 20008
 
4.2%
r 19989
 
4.2%
Other values (15) 199373
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 479543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 40014
 
8.3%
i 30208
 
6.3%
u 30011
 
6.3%
l 29992
 
6.3%
s 29992
 
6.3%
c 29975
 
6.3%
p 29867
 
6.2%
t 20114
 
4.2%
d 20008
 
4.2%
r 19989
 
4.2%
Other values (15) 199373
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 479543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 40014
 
8.3%
i 30208
 
6.3%
u 30011
 
6.3%
l 29992
 
6.3%
s 29992
 
6.3%
c 29975
 
6.3%
p 29867
 
6.2%
t 20114
 
4.2%
d 20008
 
4.2%
r 19989
 
4.2%
Other values (15) 199373
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 479543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 40014
 
8.3%
i 30208
 
6.3%
u 30011
 
6.3%
l 29992
 
6.3%
s 29992
 
6.3%
c 29975
 
6.3%
p 29867
 
6.2%
t 20114
 
4.2%
d 20008
 
4.2%
r 19989
 
4.2%
Other values (15) 199373
41.6%

App/Game Price
Real number (ℝ)

High correlation  Zeros 

Distinct4870
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.847159
Minimum0
Maximum49.990002
Zeros24856
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:23.021785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.27
Q325.66
95-th percentile45.119999
Maximum49.990002
Range49.990002
Interquartile range (IQR)25.66

Descriptive statistics

Standard deviation16.236434
Coefficient of variation (CV)1.2638151
Kurtosis-0.7014444
Mean12.847159
Median Absolute Deviation (MAD)1.27
Skewness0.88756202
Sum642357.95
Variance263.62179
MonotonicityNot monotonic
2025-04-07T09:05:23.093478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24856
49.7%
4 14
 
< 0.1%
21.88999939 14
 
< 0.1%
33.70999908 14
 
< 0.1%
42.54999924 13
 
< 0.1%
21.07999992 13
 
< 0.1%
33.09000015 13
 
< 0.1%
27.62000084 13
 
< 0.1%
48.27999878 12
 
< 0.1%
31.86000061 12
 
< 0.1%
Other values (4860) 25026
50.1%
ValueCountFrequency (%)
0 24856
49.7%
0.9900000095 2
 
< 0.1%
1 4
 
< 0.1%
1.00999999 9
 
< 0.1%
1.019999981 5
 
< 0.1%
1.029999971 6
 
< 0.1%
1.039999962 6
 
< 0.1%
1.049999952 5
 
< 0.1%
1.059999943 5
 
< 0.1%
1.070000052 6
 
< 0.1%
ValueCountFrequency (%)
49.99000168 4
< 0.1%
49.97999954 3
 
< 0.1%
49.97000122 4
< 0.1%
49.95999908 5
< 0.1%
49.95000076 6
< 0.1%
49.93999863 4
< 0.1%
49.93000031 5
< 0.1%
49.91999817 9
< 0.1%
49.90999985 5
< 0.1%
49.90000153 6
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
True
25113 
False
24887 
ValueCountFrequency (%)
True 25113
50.2%
False 24887
49.8%
2025-04-07T09:05:23.144421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
False
25019 
True
24981 
ValueCountFrequency (%)
False 25019
50.0%
True 24981
50.0%
2025-04-07T09:05:23.175263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Price Paid (with Coupon)
Real number (ℝ)

High correlation  Zeros 

Distinct4556
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.513597
Minimum0
Maximum49.990002
Zeros24856
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:23.329054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.685
Q314.87
95-th percentile33.73
Maximum49.990002
Range49.990002
Interquartile range (IQR)14.87

Descriptive statistics

Standard deviation11.79275
Coefficient of variation (CV)1.3851666
Kurtosis1.1881531
Mean8.513597
Median Absolute Deviation (MAD)0.685
Skewness1.4022617
Sum425679.85
Variance139.06896
MonotonicityNot monotonic
2025-04-07T09:05:23.410038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24856
49.7%
6.099999905 20
 
< 0.1%
4.679999828 19
 
< 0.1%
4 19
 
< 0.1%
11.10999966 18
 
< 0.1%
5.309999943 18
 
< 0.1%
6.71999979 18
 
< 0.1%
1.870000005 18
 
< 0.1%
13.82999992 18
 
< 0.1%
11.06000042 18
 
< 0.1%
Other values (4546) 24978
50.0%
ValueCountFrequency (%)
0 24856
49.7%
0.3300000131 2
 
< 0.1%
0.3400000036 2
 
< 0.1%
0.349999994 1
 
< 0.1%
0.3600000143 1
 
< 0.1%
0.3799999952 3
 
< 0.1%
0.3899999857 3
 
< 0.1%
0.400000006 1
 
< 0.1%
0.4099999964 5
 
< 0.1%
0.4199999869 6
 
< 0.1%
ValueCountFrequency (%)
49.99000168 1
 
< 0.1%
49.97999954 1
 
< 0.1%
49.97000122 1
 
< 0.1%
49.95999908 1
 
< 0.1%
49.93999863 2
< 0.1%
49.93000031 2
< 0.1%
49.91999817 2
< 0.1%
49.90000153 1
 
< 0.1%
49.88000107 3
< 0.1%
49.86999893 1
 
< 0.1%

Amount Spent on In-App Purchases
Real number (ℝ)

High correlation  Zeros 

Distinct10380
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.203438
Minimum0
Maximum200
Zeros30011
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:23.491964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330.85
95-th percentile150.2805
Maximum200
Range200
Interquartile range (IQR)30.85

Descriptive statistics

Standard deviation46.952512
Coefficient of variation (CV)1.8629408
Kurtosis3.820566
Mean25.203438
Median Absolute Deviation (MAD)0
Skewness2.1704855
Sum1260171.9
Variance2204.5384
MonotonicityNot monotonic
2025-04-07T09:05:23.573665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30011
60.0%
32.38000107 10
 
< 0.1%
47.91999817 10
 
< 0.1%
24 9
 
< 0.1%
25.46999931 9
 
< 0.1%
25.22999954 9
 
< 0.1%
26.17000008 9
 
< 0.1%
38.43000031 9
 
< 0.1%
40.34000015 9
 
< 0.1%
36.59999847 9
 
< 0.1%
Other values (10370) 19906
39.8%
ValueCountFrequency (%)
0 30011
60.0%
1 1
 
< 0.1%
1.029999971 2
 
< 0.1%
1.049999952 1
 
< 0.1%
1.059999943 2
 
< 0.1%
1.100000024 1
 
< 0.1%
1.110000014 1
 
< 0.1%
1.120000005 1
 
< 0.1%
1.139999986 2
 
< 0.1%
1.230000019 1
 
< 0.1%
ValueCountFrequency (%)
200 1
< 0.1%
199.9700012 1
< 0.1%
199.9400024 2
< 0.1%
199.9299927 1
< 0.1%
199.8999939 1
< 0.1%
199.8899994 1
< 0.1%
199.8699951 1
< 0.1%
199.8600006 1
< 0.1%
199.8399963 1
< 0.1%
199.8200073 2
< 0.1%

Time Spent (min)
Real number (ℝ)

Distinct301
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.33374
Minimum0
Maximum300
Zeros187
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:23.645103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q174
median149
Q3224
95-th percentile285
Maximum300
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation86.818823
Coefficient of variation (CV)0.58137446
Kurtosis-1.1928845
Mean149.33374
Median Absolute Deviation (MAD)75
Skewness0.0092253824
Sum7466687
Variance7537.5079
MonotonicityNot monotonic
2025-04-07T09:05:23.726834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 205
 
0.4%
34 200
 
0.4%
189 198
 
0.4%
128 197
 
0.4%
169 196
 
0.4%
237 194
 
0.4%
71 194
 
0.4%
206 190
 
0.4%
239 190
 
0.4%
10 189
 
0.4%
Other values (291) 48047
96.1%
ValueCountFrequency (%)
0 187
0.4%
1 183
0.4%
2 159
0.3%
3 167
0.3%
4 176
0.4%
5 189
0.4%
6 162
0.3%
7 157
0.3%
8 168
0.3%
9 162
0.3%
ValueCountFrequency (%)
300 185
0.4%
299 169
0.3%
298 160
0.3%
297 157
0.3%
296 167
0.3%
295 149
0.3%
294 168
0.3%
293 162
0.3%
292 164
0.3%
291 170
0.3%

Session Count
Real number (ℝ)

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.60462
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:23.798051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median26
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.425025
Coefficient of variation (CV)0.56337585
Kurtosis-1.198272
Mean25.60462
Median Absolute Deviation (MAD)12
Skewness-0.007801641
Sum1280231
Variance208.08134
MonotonicityNot monotonic
2025-04-07T09:05:23.880717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 1058
 
2.1%
41 1048
 
2.1%
44 1047
 
2.1%
50 1045
 
2.1%
43 1041
 
2.1%
24 1041
 
2.1%
21 1040
 
2.1%
45 1038
 
2.1%
23 1037
 
2.1%
17 1034
 
2.1%
Other values (40) 39571
79.1%
ValueCountFrequency (%)
1 1008
2.0%
2 963
1.9%
3 1003
2.0%
4 990
2.0%
5 983
2.0%
6 984
2.0%
7 999
2.0%
8 978
2.0%
9 1018
2.0%
10 947
1.9%
ValueCountFrequency (%)
50 1045
2.1%
49 998
2.0%
48 977
2.0%
47 982
2.0%
46 1006
2.0%
45 1038
2.1%
44 1047
2.1%
43 1041
2.1%
42 977
2.0%
41 1048
2.1%

Time Since Last Use (days)
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.01516
Minimum0
Maximum60
Zeros787
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:23.972670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median30
Q345
95-th percentile57
Maximum60
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.626159
Coefficient of variation (CV)0.58724186
Kurtosis-1.2034141
Mean30.01516
Median Absolute Deviation (MAD)15
Skewness-0.0027284516
Sum1500758
Variance310.68146
MonotonicityNot monotonic
2025-04-07T09:05:24.044078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 890
 
1.8%
40 882
 
1.8%
6 872
 
1.7%
49 871
 
1.7%
15 864
 
1.7%
60 864
 
1.7%
24 862
 
1.7%
5 861
 
1.7%
55 856
 
1.7%
39 855
 
1.7%
Other values (51) 41323
82.6%
ValueCountFrequency (%)
0 787
1.6%
1 818
1.6%
2 827
1.7%
3 842
1.7%
4 796
1.6%
5 861
1.7%
6 872
1.7%
7 844
1.7%
8 837
1.7%
9 826
1.7%
ValueCountFrequency (%)
60 864
1.7%
59 850
1.7%
58 767
1.5%
57 773
1.5%
56 812
1.6%
55 856
1.7%
54 844
1.7%
53 806
1.6%
52 804
1.6%
51 838
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
False
25086 
True
24914 
ValueCountFrequency (%)
False 25086
50.2%
True 24914
49.8%
2025-04-07T09:05:24.105091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
False
25169 
True
24831 
ValueCountFrequency (%)
False 25169
50.3%
True 24831
49.7%
2025-04-07T09:05:24.125397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Date
Date

Distinct96
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Minimum2025-01-01 00:00:00
Maximum2025-04-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-07T09:05:24.186155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:24.267667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Date

Distinct38012
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Minimum2025-04-07 00:00:01
Maximum2025-04-07 23:59:59
Invalid dates0
Invalid dates (%)0.0%
2025-04-07T09:05:24.339332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:24.422705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Day of Week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Sunday
7248 
Tuesday
7196 
Friday
7189 
Wednesday
7128 
Saturday
7107 
Other values (2)
14132 

Length

Max length9
Median length8
Mean length7.13736
Min length6

Characters and Unicode

Total characters356868
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunday
2nd rowSunday
3rd rowSaturday
4th rowMonday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Sunday 7248
14.5%
Tuesday 7196
14.4%
Friday 7189
14.4%
Wednesday 7128
14.3%
Saturday 7107
14.2%
Monday 7095
14.2%
Thursday 7037
14.1%

Length

2025-04-07T09:05:24.596599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:24.647239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sunday 7248
14.5%
tuesday 7196
14.4%
friday 7189
14.4%
wednesday 7128
14.3%
saturday 7107
14.2%
monday 7095
14.2%
thursday 7037
14.1%

Most occurring characters

ValueCountFrequency (%)
d 57128
16.0%
a 57107
16.0%
y 50000
14.0%
u 28588
8.0%
n 21471
 
6.0%
e 21452
 
6.0%
s 21361
 
6.0%
r 21333
 
6.0%
S 14355
 
4.0%
T 14233
 
4.0%
Other values (7) 49840
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 356868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 57128
16.0%
a 57107
16.0%
y 50000
14.0%
u 28588
8.0%
n 21471
 
6.0%
e 21452
 
6.0%
s 21361
 
6.0%
r 21333
 
6.0%
S 14355
 
4.0%
T 14233
 
4.0%
Other values (7) 49840
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 356868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 57128
16.0%
a 57107
16.0%
y 50000
14.0%
u 28588
8.0%
n 21471
 
6.0%
e 21452
 
6.0%
s 21361
 
6.0%
r 21333
 
6.0%
S 14355
 
4.0%
T 14233
 
4.0%
Other values (7) 49840
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 356868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 57128
16.0%
a 57107
16.0%
y 50000
14.0%
u 28588
8.0%
n 21471
 
6.0%
e 21452
 
6.0%
s 21361
 
6.0%
r 21333
 
6.0%
S 14355
 
4.0%
T 14233
 
4.0%
Other values (7) 49840
14.0%

Weekend
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
True
25066 
False
24934 
ValueCountFrequency (%)
True 25066
50.1%
False 24934
49.9%
2025-04-07T09:05:24.698255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Autumn
12631 
Summer
12535 
Spring
12443 
Winter
12391 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters300000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowSpring
3rd rowWinter
4th rowWinter
5th rowSummer

Common Values

ValueCountFrequency (%)
Autumn 12631
25.3%
Summer 12535
25.1%
Spring 12443
24.9%
Winter 12391
24.8%

Length

2025-04-07T09:05:24.749163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:24.789698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
autumn 12631
25.3%
summer 12535
25.1%
spring 12443
24.9%
winter 12391
24.8%

Most occurring characters

ValueCountFrequency (%)
u 37797
12.6%
m 37701
12.6%
n 37465
12.5%
r 37369
12.5%
t 25022
8.3%
S 24978
8.3%
e 24926
8.3%
i 24834
8.3%
A 12631
 
4.2%
p 12443
 
4.1%
Other values (2) 24834
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 37797
12.6%
m 37701
12.6%
n 37465
12.5%
r 37369
12.5%
t 25022
8.3%
S 24978
8.3%
e 24926
8.3%
i 24834
8.3%
A 12631
 
4.2%
p 12443
 
4.1%
Other values (2) 24834
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 37797
12.6%
m 37701
12.6%
n 37465
12.5%
r 37369
12.5%
t 25022
8.3%
S 24978
8.3%
e 24926
8.3%
i 24834
8.3%
A 12631
 
4.2%
p 12443
 
4.1%
Other values (2) 24834
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 37797
12.6%
m 37701
12.6%
n 37465
12.5%
r 37369
12.5%
t 25022
8.3%
S 24978
8.3%
e 24926
8.3%
i 24834
8.3%
A 12631
 
4.2%
p 12443
 
4.1%
Other values (2) 24834
8.3%

Rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
3
10182 
5
10013 
4
9995 
2
9994 
1
9816 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 10182
20.4%
5 10013
20.0%
4 9995
20.0%
2 9994
20.0%
1 9816
19.6%

Length

2025-04-07T09:05:24.840367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:24.891771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 10182
20.4%
5 10013
20.0%
4 9995
20.0%
2 9994
20.0%
1 9816
19.6%

Most occurring characters

ValueCountFrequency (%)
3 10182
20.4%
5 10013
20.0%
4 9995
20.0%
2 9994
20.0%
1 9816
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 10182
20.4%
5 10013
20.0%
4 9995
20.0%
2 9994
20.0%
1 9816
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 10182
20.4%
5 10013
20.0%
4 9995
20.0%
2 9994
20.0%
1 9816
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 10182
20.4%
5 10013
20.0%
4 9995
20.0%
2 9994
20.0%
1 9816
19.6%

Review Text
Text

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2025-04-07T09:05:25.079991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length123
Median length91
Mean length62.49428
Min length24

Characters and Unicode

Total characters3124714
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50000 ?
Unique (%)100.0%

Sample

1st rowEveryone democratic shake bill here grow gas enough analysis least.
2nd rowDetail food shoulder argue start source husband at tree note responsibility defense material.
3rd rowShoulder future fall citizen about reveal rest will seven.
4th rowRequire human public health tonight later easy ask.
5th rowArt rock song body court movie cell.
ValueCountFrequency (%)
enjoy 564
 
0.1%
plant 559
 
0.1%
building 559
 
0.1%
place 557
 
0.1%
blood 554
 
0.1%
stuff 549
 
0.1%
fine 549
 
0.1%
ask 546
 
0.1%
successful 546
 
0.1%
conference 546
 
0.1%
Other values (961) 472253
98.8%
2025-04-07T09:05:25.356580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
427782
13.7%
e 350180
 
11.2%
t 208123
 
6.7%
r 202675
 
6.5%
a 199136
 
6.4%
o 187735
 
6.0%
i 182678
 
5.8%
n 172032
 
5.5%
s 143957
 
4.6%
l 130418
 
4.2%
Other values (42) 919998
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3124714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
427782
13.7%
e 350180
 
11.2%
t 208123
 
6.7%
r 202675
 
6.5%
a 199136
 
6.4%
o 187735
 
6.0%
i 182678
 
5.8%
n 172032
 
5.5%
s 143957
 
4.6%
l 130418
 
4.2%
Other values (42) 919998
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3124714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
427782
13.7%
e 350180
 
11.2%
t 208123
 
6.7%
r 202675
 
6.5%
a 199136
 
6.4%
o 187735
 
6.0%
i 182678
 
5.8%
n 172032
 
5.5%
s 143957
 
4.6%
l 130418
 
4.2%
Other values (42) 919998
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3124714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
427782
13.7%
e 350180
 
11.2%
t 208123
 
6.7%
r 202675
 
6.5%
a 199136
 
6.4%
o 187735
 
6.0%
i 182678
 
5.8%
n 172032
 
5.5%
s 143957
 
4.6%
l 130418
 
4.2%
Other values (42) 919998
29.4%

Review Sentiment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Negative
16755 
Positive
16649 
Neutral
16596 

Length

Max length8
Median length8
Mean length7.66808
Min length7

Characters and Unicode

Total characters383404
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowNeutral
3rd rowPositive
4th rowNeutral
5th rowNegative

Common Values

ValueCountFrequency (%)
Negative 16755
33.5%
Positive 16649
33.3%
Neutral 16596
33.2%

Length

2025-04-07T09:05:25.417137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:25.469008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
negative 16755
33.5%
positive 16649
33.3%
neutral 16596
33.2%

Most occurring characters

ValueCountFrequency (%)
e 66755
17.4%
i 50053
13.1%
t 50000
13.0%
v 33404
8.7%
N 33351
8.7%
a 33351
8.7%
g 16755
 
4.4%
P 16649
 
4.3%
o 16649
 
4.3%
s 16649
 
4.3%
Other values (3) 49788
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 383404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 66755
17.4%
i 50053
13.1%
t 50000
13.0%
v 33404
8.7%
N 33351
8.7%
a 33351
8.7%
g 16755
 
4.4%
P 16649
 
4.3%
o 16649
 
4.3%
s 16649
 
4.3%
Other values (3) 49788
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 383404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 66755
17.4%
i 50053
13.1%
t 50000
13.0%
v 33404
8.7%
N 33351
8.7%
a 33351
8.7%
g 16755
 
4.4%
P 16649
 
4.3%
o 16649
 
4.3%
s 16649
 
4.3%
Other values (3) 49788
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 383404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 66755
17.4%
i 50053
13.1%
t 50000
13.0%
v 33404
8.7%
N 33351
8.7%
a 33351
8.7%
g 16755
 
4.4%
P 16649
 
4.3%
o 16649
 
4.3%
s 16649
 
4.3%
Other values (3) 49788
13.0%

Review Length
Real number (ℝ)

Distinct281
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.05168
Minimum20
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:25.526480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q190
median160
Q3230
95-th percentile286
Maximum300
Range280
Interquartile range (IQR)140

Descriptive statistics

Standard deviation80.980031
Coefficient of variation (CV)0.50596177
Kurtosis-1.1990869
Mean160.05168
Median Absolute Deviation (MAD)70
Skewness-0.0018714944
Sum8002584
Variance6557.7654
MonotonicityNot monotonic
2025-04-07T09:05:25.601241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231 219
 
0.4%
120 213
 
0.4%
259 213
 
0.4%
170 209
 
0.4%
206 208
 
0.4%
54 208
 
0.4%
50 208
 
0.4%
293 207
 
0.4%
96 205
 
0.4%
107 205
 
0.4%
Other values (271) 47905
95.8%
ValueCountFrequency (%)
20 168
0.3%
21 179
0.4%
22 201
0.4%
23 198
0.4%
24 180
0.4%
25 156
0.3%
26 151
0.3%
27 172
0.3%
28 166
0.3%
29 166
0.3%
ValueCountFrequency (%)
300 159
0.3%
299 183
0.4%
298 192
0.4%
297 159
0.3%
296 162
0.3%
295 197
0.4%
294 163
0.3%
293 207
0.4%
292 174
0.3%
291 197
0.4%
Distinct25343
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2025-04-07T09:05:25.728332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length21
Mean length12.06528
Min length5

Characters and Unicode

Total characters603264
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16355 ?
Unique (%)32.7%

Sample

1st rowSouth Christianport
2nd rowThomasberg
3rd rowPort Colleenhaven
4th rowNew Angelashire
5th rowWest Steven
ValueCountFrequency (%)
lake 3631
 
4.8%
east 3622
 
4.8%
new 3614
 
4.8%
port 3611
 
4.8%
south 3568
 
4.8%
west 3543
 
4.7%
north 3523
 
4.7%
michael 291
 
0.4%
john 204
 
0.3%
david 200
 
0.3%
Other values (14556) 49305
65.6%
2025-04-07T09:05:25.912423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 57889
 
9.6%
t 47345
 
7.8%
r 47140
 
7.8%
a 46910
 
7.8%
o 41720
 
6.9%
h 33620
 
5.6%
n 32683
 
5.4%
i 29681
 
4.9%
s 28087
 
4.7%
25112
 
4.2%
Other values (43) 213077
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 603264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 57889
 
9.6%
t 47345
 
7.8%
r 47140
 
7.8%
a 46910
 
7.8%
o 41720
 
6.9%
h 33620
 
5.6%
n 32683
 
5.4%
i 29681
 
4.9%
s 28087
 
4.7%
25112
 
4.2%
Other values (43) 213077
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 603264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 57889
 
9.6%
t 47345
 
7.8%
r 47140
 
7.8%
a 46910
 
7.8%
o 41720
 
6.9%
h 33620
 
5.6%
n 32683
 
5.4%
i 29681
 
4.9%
s 28087
 
4.7%
25112
 
4.2%
Other values (43) 213077
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 603264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 57889
 
9.6%
t 47345
 
7.8%
r 47140
 
7.8%
a 46910
 
7.8%
o 41720
 
6.9%
h 33620
 
5.6%
n 32683
 
5.4%
i 29681
 
4.9%
s 28087
 
4.7%
25112
 
4.2%
Other values (43) 213077
35.3%

State
Categorical

High correlation 

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
KwaZulu-Natal
 
1479
Western Cape
 
1442
Gauteng
 
1418
Île-de-France
 
1387
Provence-Alpes-Côte d'Azur
 
1384
Other values (43)
42890 

Length

Max length26
Median length17
Mean length10.0505
Min length5

Characters and Unicode

Total characters502525
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGauteng
2nd rowKyoto
3rd rowBeijing
4th rowOsaka
5th rowKwaZulu-Natal

Common Values

ValueCountFrequency (%)
KwaZulu-Natal 1479
 
3.0%
Western Cape 1442
 
2.9%
Gauteng 1418
 
2.8%
Île-de-France 1387
 
2.8%
Provence-Alpes-Côte d'Azur 1384
 
2.8%
Auvergne-Rhône-Alpes 1377
 
2.8%
Tokyo 1102
 
2.2%
Queensland 1101
 
2.2%
Bahia 1092
 
2.2%
Jalisco 1090
 
2.2%
Other values (38) 37128
74.3%

Length

2025-04-07T09:05:25.968479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
western 2412
 
3.6%
wales 2069
 
3.1%
new 1889
 
2.8%
kwazulu-natal 1479
 
2.2%
cape 1442
 
2.1%
gauteng 1418
 
2.1%
île-de-france 1387
 
2.0%
provence-alpes-côte 1384
 
2.0%
d'azur 1384
 
2.0%
auvergne-rhône-alpes 1377
 
2.0%
Other values (52) 51542
76.0%

Most occurring characters

ValueCountFrequency (%)
a 56174
 
11.2%
e 48085
 
9.6%
i 30873
 
6.1%
n 30642
 
6.1%
r 29006
 
5.8%
o 26599
 
5.3%
l 26545
 
5.3%
t 22383
 
4.5%
u 19346
 
3.8%
s 19001
 
3.8%
Other values (47) 193871
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 502525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 56174
 
11.2%
e 48085
 
9.6%
i 30873
 
6.1%
n 30642
 
6.1%
r 29006
 
5.8%
o 26599
 
5.3%
l 26545
 
5.3%
t 22383
 
4.5%
u 19346
 
3.8%
s 19001
 
3.8%
Other values (47) 193871
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 502525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 56174
 
11.2%
e 48085
 
9.6%
i 30873
 
6.1%
n 30642
 
6.1%
r 29006
 
5.8%
o 26599
 
5.3%
l 26545
 
5.3%
t 22383
 
4.5%
u 19346
 
3.8%
s 19001
 
3.8%
Other values (47) 193871
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 502525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 56174
 
11.2%
e 48085
 
9.6%
i 30873
 
6.1%
n 30642
 
6.1%
r 29006
 
5.8%
o 26599
 
5.3%
l 26545
 
5.3%
t 22383
 
4.5%
u 19346
 
3.8%
s 19001
 
3.8%
Other values (47) 193871
38.6%

Country
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
South Africa
4339 
India
4239 
USA
4203 
Brazil
4194 
Mexico
4190 
Other values (7)
28835 

Length

Max length12
Median length9
Mean length6.02616
Min length2

Characters and Unicode

Total characters301308
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowJapan
3rd rowChina
4th rowJapan
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa 4339
8.7%
India 4239
8.5%
USA 4203
8.4%
Brazil 4194
8.4%
Mexico 4190
8.4%
Australia 4187
8.4%
Japan 4161
8.3%
China 4155
8.3%
France 4148
8.3%
Germany 4093
8.2%
Other values (2) 8091
16.2%

Length

2025-04-07T09:05:26.030032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 4339
 
8.0%
africa 4339
 
8.0%
india 4239
 
7.8%
usa 4203
 
7.7%
brazil 4194
 
7.7%
mexico 4190
 
7.7%
australia 4187
 
7.7%
japan 4161
 
7.7%
china 4155
 
7.6%
france 4148
 
7.6%
Other values (3) 12184
22.4%

Most occurring characters

ValueCountFrequency (%)
a 53975
17.9%
i 25304
 
8.4%
n 24833
 
8.2%
r 20961
 
7.0%
A 12729
 
4.2%
c 12677
 
4.2%
e 12431
 
4.1%
S 8542
 
2.8%
o 8529
 
2.8%
t 8526
 
2.8%
Other values (21) 112801
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 301308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 53975
17.9%
i 25304
 
8.4%
n 24833
 
8.2%
r 20961
 
7.0%
A 12729
 
4.2%
c 12677
 
4.2%
e 12431
 
4.1%
S 8542
 
2.8%
o 8529
 
2.8%
t 8526
 
2.8%
Other values (21) 112801
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 301308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 53975
17.9%
i 25304
 
8.4%
n 24833
 
8.2%
r 20961
 
7.0%
A 12729
 
4.2%
c 12677
 
4.2%
e 12431
 
4.1%
S 8542
 
2.8%
o 8529
 
2.8%
t 8526
 
2.8%
Other values (21) 112801
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 301308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 53975
17.9%
i 25304
 
8.4%
n 24833
 
8.2%
r 20961
 
7.0%
A 12729
 
4.2%
c 12677
 
4.2%
e 12431
 
4.1%
S 8542
 
2.8%
o 8529
 
2.8%
t 8526
 
2.8%
Other values (21) 112801
37.4%

Region
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
North America
10074 
Europe
10026 
Southeast Asia
10000 
Africa
9962 
South America
9938 

Length

Max length14
Median length13
Mean length10.40168
Min length6

Characters and Unicode

Total characters520084
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEurope
2nd rowSoutheast Asia
3rd rowEurope
4th rowSoutheast Asia
5th rowEurope

Common Values

ValueCountFrequency (%)
North America 10074
20.1%
Europe 10026
20.1%
Southeast Asia 10000
20.0%
Africa 9962
19.9%
South America 9938
19.9%

Length

2025-04-07T09:05:26.091257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:26.142690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
america 20012
25.0%
north 10074
12.6%
europe 10026
12.5%
southeast 10000
12.5%
asia 10000
12.5%
africa 9962
12.5%
south 9938
12.4%

Most occurring characters

ValueCountFrequency (%)
r 50074
 
9.6%
a 49974
 
9.6%
e 40038
 
7.7%
o 40038
 
7.7%
t 40012
 
7.7%
i 39974
 
7.7%
A 39974
 
7.7%
30012
 
5.8%
h 30012
 
5.8%
c 29974
 
5.8%
Other values (8) 130002
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 50074
 
9.6%
a 49974
 
9.6%
e 40038
 
7.7%
o 40038
 
7.7%
t 40012
 
7.7%
i 39974
 
7.7%
A 39974
 
7.7%
30012
 
5.8%
h 30012
 
5.8%
c 29974
 
5.8%
Other values (8) 130002
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 50074
 
9.6%
a 49974
 
9.6%
e 40038
 
7.7%
o 40038
 
7.7%
t 40012
 
7.7%
i 39974
 
7.7%
A 39974
 
7.7%
30012
 
5.8%
h 30012
 
5.8%
c 29974
 
5.8%
Other values (8) 130002
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 50074
 
9.6%
a 49974
 
9.6%
e 40038
 
7.7%
o 40038
 
7.7%
t 40012
 
7.7%
i 39974
 
7.7%
A 39974
 
7.7%
30012
 
5.8%
h 30012
 
5.8%
c 29974
 
5.8%
Other values (8) 130002
25.0%

Play Pass Plan
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing12596
Missing (%)25.2%
Memory size3.0 MiB
yearly
12564 
one_month
12524 
monthly
12316 

Length

Max length9
Median length7
Mean length7.3337611
Min length6

Characters and Unicode

Total characters274312
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmonthly
2nd rowone_month
3rd rowyearly
4th rowyearly
5th rowone_month

Common Values

ValueCountFrequency (%)
yearly 12564
25.1%
one_month 12524
25.0%
monthly 12316
24.6%
(Missing) 12596
25.2%

Length

2025-04-07T09:05:26.213727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:26.254806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yearly 12564
33.6%
one_month 12524
33.5%
monthly 12316
32.9%

Most occurring characters

ValueCountFrequency (%)
y 37444
13.7%
o 37364
13.6%
n 37364
13.6%
e 25088
9.1%
l 24880
9.1%
h 24840
9.1%
m 24840
9.1%
t 24840
9.1%
a 12564
 
4.6%
r 12564
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 274312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 37444
13.7%
o 37364
13.6%
n 37364
13.6%
e 25088
9.1%
l 24880
9.1%
h 24840
9.1%
m 24840
9.1%
t 24840
9.1%
a 12564
 
4.6%
r 12564
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 274312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 37444
13.7%
o 37364
13.6%
n 37364
13.6%
e 25088
9.1%
l 24880
9.1%
h 24840
9.1%
m 24840
9.1%
t 24840
9.1%
a 12564
 
4.6%
r 12564
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 274312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 37444
13.7%
o 37364
13.6%
n 37364
13.6%
e 25088
9.1%
l 24880
9.1%
h 24840
9.1%
m 24840
9.1%
t 24840
9.1%
a 12564
 
4.6%
r 12564
 
4.6%

Play Pass User
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
True
37404 
False
12596 
ValueCountFrequency (%)
True 37404
74.8%
False 12596
 
25.2%
2025-04-07T09:05:26.285133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Subscription Duration
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.01206
Minimum0
Maximum24
Zeros1967
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-04-07T09:05:26.336110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile23
Maximum24
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1962289
Coefficient of variation (CV)0.59908366
Kurtosis-1.199153
Mean12.01206
Median Absolute Deviation (MAD)6
Skewness-0.0013179261
Sum600603
Variance51.78571
MonotonicityNot monotonic
2025-04-07T09:05:26.397126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
23 2119
 
4.2%
14 2060
 
4.1%
11 2056
 
4.1%
10 2051
 
4.1%
16 2042
 
4.1%
17 2038
 
4.1%
5 2035
 
4.1%
6 2032
 
4.1%
19 2024
 
4.0%
20 2018
 
4.0%
Other values (15) 29525
59.1%
ValueCountFrequency (%)
0 1967
3.9%
1 1977
4.0%
2 1997
4.0%
3 1978
4.0%
4 2004
4.0%
5 2035
4.1%
6 2032
4.1%
7 1930
3.9%
8 1999
4.0%
9 1954
3.9%
ValueCountFrequency (%)
24 1946
3.9%
23 2119
4.2%
22 1954
3.9%
21 1915
3.8%
20 2018
4.0%
19 2024
4.0%
18 1966
3.9%
17 2038
4.1%
16 2042
4.1%
15 1952
3.9%

Auto-Renew
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
True
25046 
False
24954 
ValueCountFrequency (%)
True 25046
50.1%
False 24954
49.9%
2025-04-07T09:05:26.437769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

App Tags
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Editor’s Choice, Multiplayer
 
2567
Family Friendly, AR Support
 
2564
Offline, Family Friendly
 
2552
Multiplayer, AR Support
 
2543
Multiplayer, Offline
 
2533
Other values (15)
37241 

Length

Max length32
Median length27
Mean length25.2183
Min length19

Characters and Unicode

Total characters1260915
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline, AR Support
2nd rowEditor’s Choice, Multiplayer
3rd rowAR Support, Family Friendly
4th rowAR Support, Offline
5th rowEditor’s Choice, Family Friendly

Common Values

ValueCountFrequency (%)
Editor’s Choice, Multiplayer 2567
 
5.1%
Family Friendly, AR Support 2564
 
5.1%
Offline, Family Friendly 2552
 
5.1%
Multiplayer, AR Support 2543
 
5.1%
Multiplayer, Offline 2533
 
5.1%
Editor’s Choice, AR Support 2530
 
5.1%
Family Friendly, Editor’s Choice 2528
 
5.1%
Editor’s Choice, Family Friendly 2527
 
5.1%
AR Support, Family Friendly 2525
 
5.1%
AR Support, Multiplayer 2513
 
5.0%
Other values (10) 24618
49.2%

Length

2025-04-07T09:05:26.489298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
family 20061
12.5%
friendly 20061
12.5%
multiplayer 20042
12.5%
choice 20026
12.5%
editor’s 20026
12.5%
ar 20017
12.5%
support 20017
12.5%
offline 19854
12.4%

Most occurring characters

ValueCountFrequency (%)
i 120070
 
9.5%
110104
 
8.7%
l 100060
 
7.9%
r 80146
 
6.4%
e 79983
 
6.3%
y 60164
 
4.8%
t 60085
 
4.8%
p 60076
 
4.8%
o 60069
 
4.8%
, 50000
 
4.0%
Other values (18) 480158
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1260915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 120070
 
9.5%
110104
 
8.7%
l 100060
 
7.9%
r 80146
 
6.4%
e 79983
 
6.3%
y 60164
 
4.8%
t 60085
 
4.8%
p 60076
 
4.8%
o 60069
 
4.8%
, 50000
 
4.0%
Other values (18) 480158
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1260915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 120070
 
9.5%
110104
 
8.7%
l 100060
 
7.9%
r 80146
 
6.4%
e 79983
 
6.3%
y 60164
 
4.8%
t 60085
 
4.8%
p 60076
 
4.8%
o 60069
 
4.8%
, 50000
 
4.0%
Other values (18) 480158
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1260915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 120070
 
9.5%
110104
 
8.7%
l 100060
 
7.9%
r 80146
 
6.4%
e 79983
 
6.3%
y 60164
 
4.8%
t 60085
 
4.8%
p 60076
 
4.8%
o 60069
 
4.8%
, 50000
 
4.0%
Other values (18) 480158
38.1%

Age Rating
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Teen
16735 
Everyone
16665 
18+
16600 

Length

Max length8
Median length4
Mean length5.0012
Min length3

Characters and Unicode

Total characters250060
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTeen
2nd row18+
3rd row18+
4th row18+
5th row18+

Common Values

ValueCountFrequency (%)
Teen 16735
33.5%
Everyone 16665
33.3%
18+ 16600
33.2%

Length

2025-04-07T09:05:26.550561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:26.591417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
teen 16735
33.5%
everyone 16665
33.3%
18 16600
33.2%

Most occurring characters

ValueCountFrequency (%)
e 66800
26.7%
n 33400
13.4%
T 16735
 
6.7%
E 16665
 
6.7%
v 16665
 
6.7%
r 16665
 
6.7%
y 16665
 
6.7%
o 16665
 
6.7%
1 16600
 
6.6%
8 16600
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 66800
26.7%
n 33400
13.4%
T 16735
 
6.7%
E 16665
 
6.7%
v 16665
 
6.7%
r 16665
 
6.7%
y 16665
 
6.7%
o 16665
 
6.7%
1 16600
 
6.6%
8 16600
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 66800
26.7%
n 33400
13.4%
T 16735
 
6.7%
E 16665
 
6.7%
v 16665
 
6.7%
r 16665
 
6.7%
y 16665
 
6.7%
o 16665
 
6.7%
1 16600
 
6.6%
8 16600
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 66800
26.7%
n 33400
13.4%
T 16735
 
6.7%
E 16665
 
6.7%
v 16665
 
6.7%
r 16665
 
6.7%
y 16665
 
6.7%
o 16665
 
6.7%
1 16600
 
6.6%
8 16600
 
6.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
pt-BR
10112 
hi-IN
10043 
en-US
10001 
de-DE
9992 
en-GB
9852 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters250000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen-US
2nd rowpt-BR
3rd rowde-DE
4th rowpt-BR
5th rowpt-BR

Common Values

ValueCountFrequency (%)
pt-BR 10112
20.2%
hi-IN 10043
20.1%
en-US 10001
20.0%
de-DE 9992
20.0%
en-GB 9852
19.7%

Length

2025-04-07T09:05:26.744359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T09:05:26.785407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pt-br 10112
20.2%
hi-in 10043
20.1%
en-us 10001
20.0%
de-de 9992
20.0%
en-gb 9852
19.7%

Most occurring characters

ValueCountFrequency (%)
- 50000
20.0%
e 29845
11.9%
B 19964
 
8.0%
n 19853
 
7.9%
p 10112
 
4.0%
t 10112
 
4.0%
R 10112
 
4.0%
h 10043
 
4.0%
i 10043
 
4.0%
N 10043
 
4.0%
Other values (7) 69873
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 50000
20.0%
e 29845
11.9%
B 19964
 
8.0%
n 19853
 
7.9%
p 10112
 
4.0%
t 10112
 
4.0%
R 10112
 
4.0%
h 10043
 
4.0%
i 10043
 
4.0%
N 10043
 
4.0%
Other values (7) 69873
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 50000
20.0%
e 29845
11.9%
B 19964
 
8.0%
n 19853
 
7.9%
p 10112
 
4.0%
t 10112
 
4.0%
R 10112
 
4.0%
h 10043
 
4.0%
i 10043
 
4.0%
N 10043
 
4.0%
Other values (7) 69873
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 50000
20.0%
e 29845
11.9%
B 19964
 
8.0%
n 19853
 
7.9%
p 10112
 
4.0%
t 10112
 
4.0%
R 10112
 
4.0%
h 10043
 
4.0%
i 10043
 
4.0%
N 10043
 
4.0%
Other values (7) 69873
27.9%

Interactions

2025-04-07T09:05:17.528670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.202442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.035094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.823682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.731458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.560897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.361349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:13.282061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:14.136397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.045838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.840423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.650638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.598791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.275598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.104204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.898220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.797888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.628175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.536485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:13.352748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:14.208844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.100995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.905807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.806876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.660855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.341480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.165343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:10.855431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.684670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.588971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:15.969763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.871031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.724276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.399837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.230987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.025821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.930640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.757120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.658958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:17.778551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:10.094506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.999516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.825180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:12.812130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:14.708253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.512815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.312177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.207041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:18.050942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.754020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.570165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:11.282303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.087567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:13.017388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-07T09:05:15.573421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.380659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.272788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:18.107723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.828659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.632957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.520446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.347718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.162277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:13.073284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:13.926492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:14.841952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.644905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.444781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.336005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:18.170671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.899484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.701465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.596873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.422396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.230391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:13.149611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:14.001704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:14.903630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.712187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.503967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.395865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:18.245120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:08.967004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:09.764177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:10.662889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:11.486364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:12.288357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:13.204905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:14.059365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:14.977647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:15.767555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:16.580268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T09:05:17.469037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-07T09:05:26.877615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAge RatingAmount Spent on In-App PurchasesAndroid VersionApp Size (MB)App TagsApp/Game PriceAuto-RenewCategoryCountryDay of WeekDevice Locale/LanguageDevice TypeDiscount AppliedFavorite FlagFree/PaidGenderIDIn-App PurchasesIncome LevelPlay Pass PlanPlay Pass UserPrice Paid (with Coupon)Promo Code UsedRatingRegionReview LengthReview SentimentSeasonSession CountStateSub_CategorySubscription DurationTime Since Last Use (days)Time Spent (min)Total InstallsTransaction TypeUninstalledWeekend
Age1.0000.000-0.0080.0000.0090.000-0.0040.0000.0100.0000.0000.0050.0040.0000.0000.0000.008-0.0030.0000.0000.0120.000-0.0050.0060.0030.000-0.0030.0000.0000.0000.0000.0090.0080.002-0.0000.0060.0090.0000.011
Age Rating0.0001.0000.0000.0000.0000.0000.0000.0080.0000.0060.0100.0000.0000.0000.0050.0070.0000.0000.0040.0000.0110.0000.0000.0000.0000.0000.0070.0010.0030.0000.0160.0110.0060.0090.0000.0000.0000.0080.000
Amount Spent on In-App Purchases-0.0080.0001.0000.000-0.0090.009-0.0040.0000.0080.0000.0000.0070.0000.0110.0000.0000.0000.0020.0000.0060.0030.003-0.0020.0000.0060.000-0.0090.0000.004-0.0070.0000.002-0.002-0.000-0.0010.0050.5360.0000.010
Android Version0.0000.0000.0001.0000.0000.0120.0000.0000.0000.0000.0060.0040.0050.0000.0040.0030.0000.0100.0070.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0090.0000.0000.0050.000
App Size (MB)0.0090.000-0.0090.0001.0000.000-0.0080.0050.0040.0040.0060.0000.0040.0000.0070.0000.000-0.0170.0070.0040.0000.006-0.0080.0180.0000.0000.0020.0000.000-0.0070.0030.0000.0070.0010.0000.0070.0100.0000.010
App Tags0.0000.0000.0090.0120.0001.0000.0020.0030.0030.0050.0080.0000.0000.0000.0130.0070.0000.0000.0000.0000.0130.0040.0000.0000.0000.0090.0100.0000.0050.0000.0000.0070.0000.0000.0070.0010.0000.0000.000
App/Game Price-0.0040.000-0.0040.000-0.0080.0021.0000.0000.0040.0010.0000.0040.0000.0000.0080.9230.0000.0020.0040.0020.0000.0000.9790.0050.0070.007-0.0090.0000.0000.0030.0050.000-0.0030.0030.0010.0060.0040.0000.000
Auto-Renew0.0000.0080.0000.0000.0050.0030.0001.0000.0110.0070.0000.0050.0000.0050.0020.0070.0020.0090.0050.0000.0000.0040.0050.0000.0000.0000.0000.0030.0000.0000.0070.0100.0110.0000.0000.0010.0060.0000.000
Category0.0100.0000.0080.0000.0040.0030.0040.0111.0000.0000.0070.0000.0000.0000.0000.0010.0000.0030.0000.0030.0030.0000.0000.0000.0000.0000.0000.0000.0000.0080.0001.0000.0090.0000.0000.0010.0070.0020.010
Country0.0000.0060.0000.0000.0040.0050.0010.0070.0001.0000.0000.0000.0000.0020.0050.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0040.0060.0000.0031.0000.0050.0000.0050.0060.0050.0040.0130.000
Day of Week0.0000.0100.0000.0060.0060.0080.0000.0000.0070.0001.0000.0000.0070.0000.0000.0000.0020.0050.0050.0060.0090.0000.0070.0000.0010.0020.0000.0000.0000.0080.0040.0080.0040.0080.0030.0030.0050.0000.008
Device Locale/Language0.0050.0000.0070.0040.0000.0000.0040.0050.0000.0000.0001.0000.0000.0130.0000.0000.0040.0000.0000.0000.0000.0060.0000.0000.0030.0000.0000.0000.0050.0050.0040.0000.0090.0000.0000.0000.0050.0000.006
Device Type0.0040.0000.0000.0050.0040.0000.0000.0000.0000.0000.0070.0001.0000.0050.0000.0070.0000.0000.0000.0000.0000.0070.0060.0000.0060.0000.0000.0040.0000.0090.0030.0000.0000.0070.0000.0000.0000.0110.000
Discount Applied0.0000.0000.0110.0000.0000.0000.0000.0050.0000.0020.0000.0130.0051.0000.0030.0000.0000.0060.0000.0040.0030.0010.2000.0030.0090.0050.0140.0050.0000.0000.0020.0190.0090.0000.0110.0000.0040.0000.004
Favorite Flag0.0000.0050.0000.0040.0070.0130.0080.0020.0000.0050.0000.0000.0000.0031.0000.0040.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0120.0000.0000.0000.004
Free/Paid0.0000.0070.0000.0030.0000.0070.9230.0070.0010.0040.0000.0000.0070.0000.0041.0000.0000.0070.0000.0000.0000.0000.8560.0070.0000.0090.0000.0000.0000.0100.0000.0000.0150.0040.0000.0100.0000.0000.000
Gender0.0080.0000.0000.0000.0000.0000.0000.0020.0000.0000.0020.0040.0000.0000.0000.0001.0000.0000.0000.0030.0000.0000.0000.0000.0060.0000.0130.0020.0000.0050.0000.0000.0060.0000.0110.0000.0000.0000.002
ID-0.0030.0000.0020.010-0.0170.0000.0020.0090.0030.0000.0050.0000.0000.0060.0000.0070.0001.0000.0170.0150.0000.0000.0010.0090.0000.0080.0050.0000.0100.0060.0000.009-0.0030.001-0.004-0.0020.0000.0080.000
In-App Purchases0.0000.0040.0000.0070.0070.0000.0040.0050.0000.0000.0050.0000.0000.0000.0000.0000.0000.0171.0000.0000.0090.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0060.0040.0140.0100.0070.0000.0020.000
Income Level0.0000.0000.0060.0030.0040.0000.0020.0000.0030.0000.0060.0000.0000.0040.0000.0000.0030.0150.0001.0000.0120.0030.0000.0000.0030.0050.0000.0000.0000.0090.0110.0000.0000.0130.0000.0040.0000.0020.002
Play Pass Plan0.0120.0110.0030.0000.0000.0130.0000.0000.0030.0000.0090.0000.0000.0030.0000.0000.0000.0000.0090.0121.0001.0000.0150.0060.0060.0090.0060.0000.0000.0090.0040.0000.0060.0000.0030.0000.0000.0000.002
Play Pass User0.0000.0000.0030.0000.0060.0040.0000.0040.0000.0000.0000.0060.0070.0010.0000.0000.0000.0000.0000.0031.0001.0000.0020.0000.0000.0000.0000.0030.0050.0000.0000.0000.0000.0050.0000.0000.0000.0000.000
Price Paid (with Coupon)-0.0050.000-0.0020.000-0.0080.0000.9790.0050.0000.0000.0070.0000.0060.2000.0000.8560.0000.0010.0000.0000.0150.0021.0000.2030.0030.000-0.0080.0090.0000.0030.0000.000-0.0040.0030.0010.0060.0040.0060.009
Promo Code Used0.0060.0000.0000.0000.0180.0000.0050.0000.0000.0000.0000.0000.0000.0030.0060.0070.0000.0090.0000.0000.0060.0000.2031.0000.0000.0000.0000.0000.0000.0000.0050.0000.0040.0110.0070.0110.0000.0000.000
Rating0.0030.0000.0060.0000.0000.0000.0070.0000.0000.0000.0010.0030.0060.0090.0000.0000.0060.0000.0080.0030.0060.0000.0030.0001.0000.0070.0000.0060.0040.0030.0000.0030.0000.0000.0000.0050.0050.0000.003
Region0.0000.0000.0000.0000.0000.0090.0070.0000.0000.0070.0020.0000.0000.0050.0000.0090.0000.0080.0000.0050.0090.0000.0000.0000.0071.0000.0000.0020.0000.0050.0080.0000.0110.0000.0000.0000.0030.0000.003
Review Length-0.0030.007-0.0090.0000.0020.010-0.0090.0000.0000.0040.0000.0000.0000.0140.0000.0000.0130.0050.0000.0000.0060.000-0.0080.0000.0000.0001.0000.0000.000-0.0020.0060.0000.0030.0040.0060.0100.0040.0100.000
Review Sentiment0.0000.0010.0000.0000.0000.0000.0000.0030.0000.0060.0000.0000.0040.0050.0000.0000.0020.0000.0000.0000.0000.0030.0090.0000.0060.0020.0001.0000.0000.0000.0000.0000.0000.0030.0000.0030.0000.0000.002
Season0.0000.0030.0040.0000.0000.0050.0000.0000.0000.0000.0000.0050.0000.0000.0060.0000.0000.0100.0000.0000.0000.0050.0000.0000.0040.0000.0000.0001.0000.0000.0000.0000.0000.0050.0040.0000.0060.0000.000
Session Count0.0000.000-0.0070.000-0.0070.0000.0030.0000.0080.0030.0080.0050.0090.0000.0000.0100.0050.0060.0000.0090.0090.0000.0030.0000.0030.005-0.0020.0000.0001.0000.0040.007-0.0040.003-0.002-0.0020.0000.0000.012
State0.0000.0160.0000.0000.0030.0000.0050.0070.0001.0000.0040.0040.0030.0020.0000.0000.0000.0000.0000.0110.0040.0000.0000.0050.0000.0080.0060.0000.0000.0041.0000.0000.0080.0130.0030.0020.0000.0170.000
Sub_Category0.0090.0110.0020.0000.0000.0070.0000.0101.0000.0050.0080.0000.0000.0190.0000.0000.0000.0090.0060.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0070.0001.0000.0050.0000.0000.0000.0000.0000.007
Subscription Duration0.0080.006-0.0020.0000.0070.000-0.0030.0110.0090.0000.0040.0090.0000.0090.0000.0150.006-0.0030.0040.0000.0060.000-0.0040.0040.0000.0110.0030.0000.000-0.0040.0080.0051.0000.0110.002-0.0000.0000.0080.008
Time Since Last Use (days)0.0020.009-0.0000.0050.0010.0000.0030.0000.0000.0050.0080.0000.0070.0000.0000.0040.0000.0010.0140.0130.0000.0050.0030.0110.0000.0000.0040.0030.0050.0030.0130.0000.0111.0000.0020.0030.0000.0070.000
Time Spent (min)-0.0000.000-0.0010.0090.0000.0070.0010.0000.0000.0060.0030.0000.0000.0110.0120.0000.011-0.0040.0100.0000.0030.0000.0010.0070.0000.0000.0060.0000.004-0.0020.0030.0000.0020.0021.0000.0010.0030.0040.004
Total Installs0.0060.0000.0050.0000.0070.0010.0060.0010.0010.0050.0030.0000.0000.0000.0000.0100.000-0.0020.0070.0040.0000.0000.0060.0110.0050.0000.0100.0030.000-0.0020.0020.000-0.0000.0030.0011.0000.0000.0000.016
Transaction Type0.0090.0000.5360.0000.0100.0000.0040.0060.0070.0040.0050.0050.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0050.0030.0040.0000.0060.0000.0000.0000.0000.0000.0030.0001.0000.0000.000
Uninstalled0.0000.0080.0000.0050.0000.0000.0000.0000.0020.0130.0000.0000.0110.0000.0000.0000.0000.0080.0020.0020.0000.0000.0060.0000.0000.0000.0100.0000.0000.0000.0170.0000.0080.0070.0040.0000.0001.0000.000
Weekend0.0110.0000.0100.0000.0100.0000.0000.0000.0100.0000.0080.0060.0000.0040.0040.0000.0020.0000.0000.0020.0020.0000.0090.0000.0030.0030.0000.0020.0000.0120.0000.0070.0080.0000.0040.0160.0000.0001.000

Missing values

2025-04-07T09:05:18.457049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-07T09:05:18.895030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDNameEmailPhoneAgeGenderIncome LevelDevice TypeAndroid VersionApp NameDeveloperCategorySub_CategoryFree/PaidIn-App PurchasesApp Size (MB)Total InstallsTransaction IDTransaction TypeApp/Game PriceDiscount AppliedPromo Code UsedPrice Paid (with Coupon)Amount Spent on In-App PurchasesTime Spent (min)Session CountTime Since Last Use (days)Favorite FlagUninstalledDateTimeDay of WeekWeekendSeasonRatingReview TextReview SentimentReview LengthDemographic LocationStateCountryRegionPlay Pass PlanPlay Pass UserSubscription DurationAuto-RenewApp TagsAge RatingDevice Locale/Language
01Danielle Taylorrhodespatricia@garza.com833-589-083818MaleLowMotorola Edge11Sharable Bifurcated Algorithm ActionLawrence-PachecoGamesActionFreeYes126.220001234205728df6ec4-ce4a-4bbd-8241-330b01a9e71fInstall0.000000YesNo0.0000000.002792714NoNo2025-03-0903:18:32SundayYesSummer4Everyone democratic shake bill here grow gas enough analysis least.Neutral162South ChristianportGautengSouth AfricaEuropeNaNNo6NoOffline, AR SupportTeenen-US
12Michele Williamskendragalloway@gmail.com664-375-255353OtherMediumMotorola Edge12Self-Enabling Regional Hierarchy Online CoursesReid, Ferguson and SanchezEducationOnline CoursesFreeYes404.5299997708870fd5166e6-451b-4cf3-a123-fdf77656af72Refund0.000000NoYes0.0000000.00234314NoYes2025-03-3106:43:32SundayYesSpring4Detail food shoulder argue start source husband at tree note responsibility defense material.Neutral252ThomasbergKyotoJapanSoutheast AsiamonthlyYes5NoEditor’s Choice, Multiplayer18+pt-BR
23Nicole Pattersonjeffrey28@yahoo.com801-922-691656FemaleLowPixel 711Fully-Configurable Value-Added Open Architecture Kids LearningKing, Tran and DunlapEducationKids LearningFreeNo89.7099994108245ae849217-1d53-434b-b881-39b9ae270da7Subscription0.000000NoNo0.00000021.25331458NoYes2025-03-2711:16:42SaturdayNoWinter4Shoulder future fall citizen about reveal rest will seven.Positive155Port ColleenhavenBeijingChinaEuropeone_monthYes7NoAR Support, Family Friendly18+de-DE
34Christopher Ashleygeorgetracy@gmail.com(443)903-9117x18237FemaleHighXiaomi 1313Robust Dedicated Collaboration To-Do ListMartin, Rose and ObrienProductivityTo-Do ListFreeYes249.2899937914818a0f4efb-edcd-465e-b638-6821f6e07cc0Ad Click0.000000YesYes0.0000000.00584456NoNo2025-03-1512:50:29MondayNoWinter2Require human public health tonight later easy ask.Neutral21New AngelashireOsakaJapanSoutheast AsiaNaNNo16YesAR Support, Offline18+pt-BR
45Amy Jonesheathchad@ramirez.com+1-329-397-376336FemaleLowSamsung Galaxy S2312Face-To-Face 24Hour Archive Language LearningJordan, Henderson and OwensEducationLanguage LearningFreeYes80.6600042711343a748dbcf-ac61-4e63-8dde-29a6baa4b71aInstall0.000000NoNo0.0000000.00434731YesYes2025-04-0621:14:08TuesdayNoSummer3Art rock song body court movie cell.Negative236West StevenKwaZulu-NatalSouth AfricaEuropeNaNNo17YesEditor’s Choice, Family Friendly18+pt-BR
56Krista Williamsnovaksara@gmail.com(267)873-602658OtherLowPixel 711Compatible Leadingedge Workforce RPGHouse-GloverGamesRPGFreeYes116.2300035673134f512c4c3-b253-4218-ac4a-37ea490617f2Install0.000000YesYes0.0000000.00169532YesNo2025-03-1213:21:11SaturdayNoSummer5Product main couple design around save article finish anyone live try most arm.Positive262Port JacoblandAlbertaCanadaEuropeNaNNo15NoMultiplayer, OfflineEveryonede-DE
67Steve Sanchezharveyrobert@hotmail.com001-485-643-534628MaleLowMotorola Edge14Synergistic Disintermediate Initiative Battery SaverStewart-WaltonToolsBattery SaverFreeYes432.6000069098419f044aed-7552-4327-8262-7f7312922f83Subscription0.000000YesNo0.00000022.241423015YesNo2025-02-2401:08:26SundayYesSpring5Mrs media car give attention each.Positive67EricsideHokkaidoJapanEuropeNaNNo5NoFamily Friendly, AR SupportEveryonede-DE
78Dr. Kendra Contrerassarah12@yahoo.com518-624-493522MaleMediumPixel 711Triple-Buffered Exuding Pricing Structure File ManagerFreeman, Whitehead and MathisToolsFile ManagerPaidYes492.7300114450368d450281c-6c6f-4633-a260-772317a0df49Refund35.119999NoNo35.1199990.0016043NoYes2025-01-2718:46:46MondayYesSummer1Four capital woman claim kind relationship.Negative54North CharlesbergTexasUSAEuropeyearlyYes12YesAR Support, Multiplayer18+en-GB
89Brittany Andersonmichael05@gmail.com+1-731-858-6923x2260221OtherHighOnePlus 1114Quality-Focused 5Thgeneration Hub CasualShields, Cochran and AdamsGamesCasualPaidNo330.399994948393461ee411a-1bac-47a7-b386-f7a4c991603fSubscription33.810001NoYes15.45000046.5937129YesYes2025-03-2612:07:33FridayYesAutumn2Develop staff least figure somebody dinner age cover foreign ten whom evidence.Neutral55FraziersideIllinoisUSAEuropeyearlyYes11NoMultiplayer, Family Friendly18+pt-BR
910Debbie Carpenterbenjamin96@chandler-edwards.org316.793.4060x8835630FemaleHighPixel 713Managed 4Thgeneration Budgetary Management Online CoursesEdwards, Charles and CortezEducationOnline CoursesFreeYes466.2200011738893aab97e49-4f2d-4796-81d2-c7de4ce1eb90Install0.000000NoYes0.0000000.001353331NoYes2025-02-0907:40:50MondayNoAutumn1Recent never court professor here security community notice image street fight.Positive190South KristiMexico CityMexicoEuropeone_monthYes20NoMultiplayer, Family Friendly18+de-DE
IDNameEmailPhoneAgeGenderIncome LevelDevice TypeAndroid VersionApp NameDeveloperCategorySub_CategoryFree/PaidIn-App PurchasesApp Size (MB)Total InstallsTransaction IDTransaction TypeApp/Game PriceDiscount AppliedPromo Code UsedPrice Paid (with Coupon)Amount Spent on In-App PurchasesTime Spent (min)Session CountTime Since Last Use (days)Favorite FlagUninstalledDateTimeDay of WeekWeekendSeasonRatingReview TextReview SentimentReview LengthDemographic LocationStateCountryRegionPlay Pass PlanPlay Pass UserSubscription DurationAuto-RenewApp TagsAge RatingDevice Locale/Language
4999049991Cynthia Hullerika93@hotmail.com940.934.1005x9191453MaleMediumOnePlus 1113Ameliorated Uniform Leverage CalendarRowland GroupProductivityCalendarPaidYes195.029999666336550466b70-6441-490f-9651-180935ca223fInstall4.98NoYes3.0900000.0000001381043YesYes2025-02-1412:05:26ThursdayYesSummer2Success body peace baby let relationship mouth meet face any light expect game.Negative38VictortonFloridaUSAEuropeNaNNo14YesAR Support, OfflineTeenen-GB
4999149992Ellen Shieldsnicholas26@gmail.com215-446-3583x57530OtherHighMotorola Edge11Triple-Buffered Homogeneous Artificial Intelligence PuzzleSchmidt, Nguyen and FriedmanGamesPuzzlePaidNo88.54000172298389c7f692a-63ae-4044-b05b-1a7402d529a9Refund25.41NoYes12.1700000.0000002431330NoNo2025-01-0209:35:58MondayYesSpring3Phone help baby war above much study.Neutral142West AshleyOntarioCanadaSoutheast AsiaNaNNo0YesMultiplayer, AR SupportTeenen-GB
4999249993Ann Cantrellstephen81@example.com595-245-2350x32139FemaleMediumOnePlus 1112Reactive 24Hour Frame To-Do ListJenkins, Rubio and LoweProductivityTo-Do ListFreeNo382.6700138439346aab88976-2f0d-412c-a536-2fc876b8d150Refund0.00YesYes0.0000000.0000002751416NoYes2025-02-2420:20:59WednesdayNoSummer2Mean tonight meet machine plant defense four investment space board.Positive44ToddmouthKarnatakaIndiaAfricayearlyYes17NoOffline, MultiplayerEveryonept-BR
4999349994Jon Dayamanda34@yahoo.com858.569.8874x7433861MaleHighOnePlus 1113Virtual Responsive Application PuzzleShepherd, Haynes and SmithGamesPuzzleFreeYes110.62999788766650ab343b8-af57-4a87-8432-fc7aa6ae49b4Ad Click0.00NoYes0.0000000.000000294110YesYes2025-02-1302:35:19FridayYesWinter4Key fine table method rule choose article simple ask.Neutral165Lake CaitlinTokyoJapanNorth Americaone_monthYes12NoAR Support, Multiplayer18+de-DE
4999449995Danielle Perryhensleyalfred@hotmail.com001-507-660-2806x823963FemaleMediumPixel 713Organic 4Thgeneration Groupware CalendarManning-KellyProductivityCalendarFreeNo225.0000008052425399b1843-37a9-46b4-aff1-d0f30e7cee82In-App Purchase0.00NoYes0.000000136.699997296457YesNo2025-01-3007:34:15WednesdayNoWinter2Hard market officer pretty debate land entire third three our buy.Neutral235RamseyburghWalesUKNorth AmericayearlyYes18YesAR Support, MultiplayerEveryonehi-IN
4999549996Rebecca Smithydavis@odom-warren.com+1-375-438-422559OtherLowPixel 712Compatible 4Thgeneration Project To-Do ListGreen, Matthews and HallProductivityTo-Do ListFreeNo498.5299998183199655079c7-bab6-4f98-a850-41f374e20b5bInstall0.00YesYes0.0000000.000000108304YesNo2025-02-0822:50:13SundayYesWinter3Along way enough teacher reach environment local.Neutral25Port DaniellehavenBahiaBrazilSoutheast Asiaone_monthYes24YesMultiplayer, AR SupportEveryonept-BR
4999649997Ryan Montgomeryusanchez@garcia.com+1-976-656-4738x840830OtherMediumSamsung Galaxy S2314Ergonomic Systemic Project File ManagerMorris LtdToolsFile ManagerPaidNo477.0599988797413f5fc9845-425c-4104-92e5-6d47844578ecRefund34.84YesNo15.0400000.0000001914931YesNo2025-03-3017:37:11MondayNoWinter2Evening not these far reflect shake herself wall.Neutral202West JenniferstadMinas GeraisBrazilSoutheast AsiamonthlyYes19YesEditor’s Choice, MultiplayerTeenpt-BR
4999749998David Mcgrathkimberly88@ellison.info+1-582-203-1157x173159OtherLowPixel 711Compatible Exuding Toolset RPGOlson-FarmerGamesRPGFreeNo248.3999941016133ae1ec747-1b97-421a-b98d-d41bac9c7f8aRefund0.00YesYes0.0000000.0000002543651YesYes2025-03-1721:12:14TuesdayNoAutumn5National lay just tend main road.Positive170Port BriannamouthJaliscoMexicoAfricayearlyYes4NoOffline, Editor’s ChoiceEveryonede-DE
4999849999Kaitlyn Bennettcheyennealexander@snyder.com001-374-882-2348x91946MaleMediumOnePlus 1111Inverse Full-Range Firmware RPGCook, Crawford and HowellGamesRPGPaidYes82.23999876840235dd08bcd-ad1b-4111-ad7f-1c10887c9751Install13.77YesYes6.5000000.000000412843YesNo2025-03-2016:23:16WednesdayYesWinter2Off occur maybe article according program.Positive192JohntonSão PauloBrazilEuropeone_monthYes10NoAR Support, OfflineEveryonede-DE
4999950000Darren Romeroangelarogers@example.org+1-776-923-0318x9361245FemaleHighMotorola Edge13Business-Focused Multimedia Migration Battery SaverBoyle LLCToolsBattery SaverPaidNo186.520004837245043d0a77d-5680-4454-9a12-2ecdebdb8bc7Refund31.67NoYes19.5499990.0000001112016YesNo2025-04-0203:16:24WednesdayNoSpring1Ask ok number letter dinner house attorney page yet yeah forget property still.Negative97East RonaldportMaharashtraIndiaSoutheast AsiayearlyYes7YesFamily Friendly, AR Support18+en-US